How to model categorical variables / enums?
Authors use many different approaches.

One approach is to have a different input neuron for each possible category, and then use a "1-hot" encoding. So if you have 10 categories, then you can encode this as 10 binary features.

Another is to use some sort of binary encoding. If you have 10 categories, it is sufficient to use 4 neurons to represent all possible categories by using binary numbers.

A third approach is to convert your categories to cardinal values, and then normalize them. This may be more effective if your categories really are cardinal (i.e. orderable). If there isn't a natural ordering to them though, this might lead to strange results or make the problem difficult to learn (since it ends up embedding non-linear relationships in the learning problem that don't need to exist).